@InProceedings{TaquaryPareSilvFerr:2019:NoApRe,
               author = "Taquary, Evandro and Parente, Leandro Leal and Silva, Ana Paula 
                         Matos e and Ferreira, Laerte Guimar{\~a}es",
          affiliation = "{Universidade Federal de Goi{\'a}s (UFG)} and {Universidade 
                         Federal de Goi{\'a}s (UFG)} and {Universidade Federal de 
                         Goi{\'a}s (UFG)} and {Universidade Federal de Goi{\'a}s (UFG)}",
                title = "A novel approach to recognizing patterns in remote sensing 
                         time-series using deep learning",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "3365--3368",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "deep learning, recurrent neural networks, high resolution imagery, 
                         planet images, pasturelands.",
             abstract = "One of the most remarkable breakthroughs of Remote Sensing lies 
                         upon the devise of CubeSat standard. Such technology open up a 
                         myriad of possible applications that benefit from the higher 
                         spatio-temporal resolutions delivered by constellations of CubeSat 
                         compliant nanosatellites. Within this scenario, one has to 
                         investigate the new challenges and how to tackle them in order to 
                         harness this new kind of Remote Sensing Big Data. Among these 
                         challenges is the development of the means to extract useful 
                         information of pixels' observations throughout time in a 
                         fine-grained fashion. This work is a seminal study on using a 
                         special kind of deep learning approach, namely, deep Recurrent 
                         Neural Networks, for classifying long time-series of landcover's 
                         observations. The method was tested against the problem of 
                         identifying pastureland areas over high-res imagery from 
                         PlaneScope, a constellation of CubeSat nanosatellites. A 
                         discussion concerning limitations and capabilities of the proposed 
                         approach are also presented.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3TUEM6H",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3TUEM6H",
           targetfile = "97212.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "11 maio 2024"
}